Bu çalışmada rahim ağzı kanseri ve hastalığın teşhisi hakkında bilgi veren İstanbul Zeynep Kâmil Kadın ve Çocuk Hastalıkları Eğitim ve Araştırma Hastanesi Patoloji Bölümü’ne teşekkür ederim.
Pap-smear test is used to detect cervical cancer, which ranks fourth in the ranking of cancer diseases in women worldwide. In this study, it is aimed to design a computer based decision system that can detect cervical cancer at an early stage. Normal and abnormal cells are found in the cervix images obtained as a result of the pap-smear test and the abnormal cells are marked on the image. The features extracted from the images were examined with pathologists and a dataset was created. For each of the 917 images in the Herlev dataset, these features were extracted and stored in a dataset. Support Vector Machines (SVM), Naive Bayes, Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), K- Nearest Neighbor (KNN) methods were applied to the created dataset, and accuracy values between 83% and 92% were obtained.
Birincil Dil | İngilizce |
---|---|
Konular | Yapay Zeka |
Bölüm | Makaleler |
Yazarlar | |
Yayımlanma Tarihi | 28 Ağustos 2020 |
Gönderilme Tarihi | 18 Nisan 2020 |
Kabul Tarihi | 21 Mayıs 2020 |
Yayımlandığı Sayı | Yıl 2020Cilt: 3 Sayı: 2 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License